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Semiparametric estimation of panel data models without monotonicity or separability

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  • Chen, Songnian
  • Wang, Xi

Abstract

Nonseparable panel data models with fixed effects have received a great deal of attention in the literature. Monotonicity is a common assumption in these settings, which may be violated in practice. Monotonicity-based estimators are inconsistent and the associated inference misleading under misspecification. In this paper, we propose some semiparametric estimators without imposing the monotonicity restriction. Under regularity conditions, our estimators are consistent and asymptotically normal. Our simulation suggests that our estimators work well in finite samples.

Suggested Citation

  • Chen, Songnian & Wang, Xi, 2018. "Semiparametric estimation of panel data models without monotonicity or separability," Journal of Econometrics, Elsevier, vol. 206(2), pages 515-530.
    • Handle: RePEc:eee:econom:v:206:y:2018:i:2:p:515-530
    DOI: 10.1016/j.jeconom.2018.06.012
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    References listed on IDEAS

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    Cited by:

    1. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Other publications TiSEM 7899deb9-0eda-47e6-a3b8-2, Tilburg University, School of Economics and Management.
    2. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

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    More about this item

    Keywords

    Panel data; Fixed effects; Nonseparable models;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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